改进蚁群算法的环境能源采集型WSN多目标路由研究  

Improved Ant Colony Optimization Algorithm for Multi-object Routing in Energy Harvesting Wireless Sensor Networks

作　　者：曹迪 谢敏杰 黄霆豪 雷艳静[1] 李伟[1] 周加峰 CAO Di;XIE Min-jie;HUANG Ting-hao;LEI Yan-jing;LI Wei;ZHOU Jia-feng(School of Computer Science and Technology,Zhejiang University of Technology,Hangzhou 310023,China;University of Liverpool,Liverpool UK)

机构地区：[1]浙江工业大学计算机科学与技术学院,杭州310023 [2]英国利物浦大学电气工程系,利物浦英国

出　　处：《小型微型计算机系统》2021年第5期1115-1120,共6页Journal of Chinese Computer Systems

基　　金：浙江省科技厅公益科技基金项目(2017C33108)资助.

摘　　要：环境能源采集技术为无线传感器网络的可持续自供电提供了新的能源解决方案.由于环境能源的能量密度在空间位置分布上存在随机性,且能量采集节点在同一位置的不同时刻采集功率也随机发生变化,这些无疑对环境能源采集型无线传感器网络的长期稳定运行带来了影响.为此本文提出一种基于能量状态信息的改进蚁群路由算法(ESMACO).该算法综合分析了能源采集型无线传感器网络中节点的地理位置、荷电状态及采集状态变化等多个信息,通过引入能量波动指标对蚁群路由的候选集进行了分级筛选.并且该路由算法在启发因子及信息素更新策略中引入了多目标优化的机制,令改进后的路由在能效、网络能量均衡及生存期多个方面得到平衡优化.通过仿真表明:改进后的蚁群路由算法在有效提高能源利用率的同时,较其他算法收敛速度更快,并且网络节点的能量均衡度也得到较好的提升.Energy harvesting technology provides a new sustainable energy solution for wireless sensor networks.However,due to the randomness of the spatial distribution of environmental energy and the randomness of the varying collection power with the time,it is still a key issue for the long-term stable operation of Wireless Sensor Networks.In this paper,an improved ant colony optimization(ACO)algorithm is proposed which selects the candidate set of ant colony routing node with a comprehensive consideration of the characteristics in energy harvesting wireless sensor networks(EH-WSNs),the geographical location of nodes,State-of-charge(SOC)of the battery and the changes of energy harvesting state.Meanwhile,a novel strategy for updating the heuristic factor and Pheromone has also been introduced to make a balanced optimization over multiple aspects.The final simulation results prove that the improved ant colony routing algorithm can increase the convergence speed and improve the energy balance of EH-WSNs.

关 键 词：能源采集 无线传感器网络 多目标优化 蚁群算法 能量均衡 

分 类 号：TP393[自动化与计算机技术—计算机应用技术]